# Debugging

**Step 1: Check Logs**

```bash
# View agent logs
tail -f logs/agent.log

# Check errors
tail -f logs/errors.log

# Filter by time
grep "2025-01-15" logs/agent.log
```

**Step 2: Test API Connections**

```python
# Test LLM API
from agent_builder_framework import test_llm_connection
test_llm_connection('anthropic', api_key)

# Test platform API
from agent_builder_framework import test_platform_connection
test_platform_connection('twitter', credentials)
```

**Step 3: Validate Configuration**

```bash
# Check environment variables
python agent_[ID].py --check-config

# Validate API keys
python agent_[ID].py --validate-keys

# Test mode (no actual posts)
python agent_[ID].py --test
```

**Step 4: Monitor Resources**

```bash
# Check memory usage
ps aux | grep python

# Monitor network
netstat -an | grep ESTABLISHED

# View process info
top -p $(pgrep -f agent_)
```

#### Getting Help

**Documentation**:

* Read this documentation thoroughly
* Check README files
* Review code comments

**Community Support**:

* Discord community
* GitHub discussions
* Twitter/X: @GraceOS

**Direct Support**:

* Email: <support@grace.ai>
* Priority support: <enterprise@grace.ai>
* Bug reports: GitHub Issues

**Information to Include**:

1. Agent ID
2. Error messages
3. Transaction hash (if payment related)
4. Steps to reproduce
5. Browser/system information
6. Screenshots (if applicable)

***

### Performance Optimization

#### LLM Cost Management

**Token Usage**:

Understanding token costs:

* Input tokens: Usually cheaper
* Output tokens: Usually more expensive
* Context window: Affects performance

**Optimization Strategies**:

1. **Prompt Optimization**:
   * Shorter, clearer prompts
   * Remove redundant instructions
   * Use system messages efficiently
   * Cache stable prompts
2. **Response Length Control**:
   * Set max\_tokens appropriately
   * Use stop sequences
   * Implement length checking
   * Truncate when necessary
3. **Model Selection**:
   * Use smaller models for simple tasks
   * Reserve powerful models for complex queries
   * Implement tiered response system
   * Monitor cost per interaction

**Cost Tracking**:

```python
# Example cost tracking
class CostTracker:
    def __init__(self):
        self.total_cost = 0
        self.interactions = 0
    
    def log_usage(self, model, input_tokens, output_tokens):
        cost = calculate_cost(model, input_tokens, output_tokens)
        self.total_cost += cost
        self.interactions += 1
        
    def get_average_cost(self):
        return self.total_cost / self.interactions if self.interactions > 0 else 0
```

#### Caching Strategies

**Response Caching**:

Cache frequently asked questions:

```python
from functools import lru_cache
import hashlib

class ResponseCache:
    def __init__(self, max_size=1000):
        self.cache = {}
        self.max_size = max_size
    
    def get_cache_key(self, query):
        return hashlib.md5(query.encode()).hexdigest()
    
    def get(self, query):
        key = self.get_cache_key(query)
        return self.cache.get(key)
    
    def set(self, query, response):
        if len(self.cache) >= self.max_size:
            # Remove oldest entry
            oldest = min(self.cache.keys())
            del self.cache[oldest]
        key = self.get_cache_key(query)
        self.cache[key] = response
```

**API Response Caching**:

* Cache platform API responses
* Implement TTL (Time To Live)
* Use Redis for distributed caching
* Clear cache strategically

#### Scaling Strategies

**Horizontal Scaling**:

* Multiple agent instances
* Load balancing
* Distributed processing
* Shared state management

**Vertical Scaling**:

* Increase memory
* More CPU cores
* Faster network
* SSD storage

**Queue-Based Processing**:

```python
import queue
import threading

class AgentQueue:
    def __init__(self, num_workers=4):
        self.queue = queue.Queue()
        self.workers = []
        for _ in range(num_workers):
            worker = threading.Thread(target=self.process_queue)
            worker.start()
            self.workers.append(worker)
    
    def add_task(self, task):
        self.queue.put(task)
    
    def process_queue(self):
        while True:
            task = self.queue.get()
            try:
                task.execute()
            finally:
                self.queue.task_done()
```
